code: def _train_epochs(
      self,
      data_creator,
      epochs=1,
      batch_size=32,
      profile=False,
      info=None,
  ):
      params = dict(
          data_creator=data_creator,
          epochs=epochs,
          batch_size=batch_size,
          profile=profile,
          info=info,
      )
      remote_worker_stats = []
      for i, w in enumerate(self.remote_workers):
          stats = w.train_epochs.remote(**params)
          remote_worker_stats.append(stats)
  
      success = check_for_failure(remote_worker_stats)
      if success:
          return success, ray.get(remote_worker_stats)
      else:
          return success, None
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 4 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj._train_epochs(data_creator, epochs, batch_size, profile, info) and returns: success,  ray, success,  None